A user point of view on core inflation measures

Laurent BilkeEuro Area Macroeconomic Developments Division

ECE/ILO meeting on CPIGeneve, 11 May 2006

Introduction

The need to remove short-term noise

Different ways to cope with this

To refer to core inflation measures is one of them

Outline

(1) Defining the noise with statistical criteria

(2) Three possible goals assigned to CI measures

(3) Assessing the properties of CI measures

(4) Illustration

(1) Statistical criteria

• Statistical vs model based approaches

• How to define the noise?

• First possibility: noise= sector specific developments

– Most volatile items removed

– Idiosyncratic component identified and removed

– Extreme changes removed

(1) Statistical criteria

• Second possibility: noise = less lasting developments

We remove the items with the lowest persistence

• Persistence

• Relationship persistence – variance, variance of the overall is:

> For a given item specific noise, low persistence implies low variance

(1) Statistical criteria

(1) Statistical criteria

• Two pitfalls:

(1) On the danger to exclude more than the noise

Trimmed mean: we can exclude some long-term evolutions

High tech goods or clothing

(2) On the danger to exclude once for ever

The case of energy

(1) Statistical criteria

Oil prices still pure noise?

Less volatile More persistent

(2) Three possible goals

• An indicator of current and future trends in inflation

• More or less emphasis can be placed on one side or the other (retrospective / prospective analysis)

• Emphasis on very short term developments: to remove the developments that we presume will be soon reverted – descriptive approach

• To predict inflation developments, x months ahead – leading indicator

• An intermediate: to point towards the trend, attractor

(3) How to assess the properties of CI?

• Depends on the assigned goal

• Descriptive approach

– Statistical criteria

• Same average than headline

• Lower variance

– Communication (Wyne, 99)

• Computable in real time

• Track record

• Understandable

• Stable in time

(3) How to assess the properties of CI?

• Trend/attractor approach

• Robalo Marques et al. (2003) 3 conditions

(1) Core and headline inflation have the same trend

– Cointegrated with unit coefficient

(2) CI is an attractor for headline inflation

– CI Granger causes headline through en ECM

(3) How to assess the properties of CI?

(3) Headline inflation is not an attractor for CI

– CI does not Granger causes headline through an ECM

+ through short-term dynamics (strong exogeneity)

(3) How to assess the properties of CI?

• CI as a leading indicator

– Out of sample forecast error

(4) An illustration

• Three monthly CI measures for the EA:

– HICPX: HICP excluding unprocessed food and energy

– Trimmed mean, 16% on each side

– Dynamic factor model, Cristadoro et al. (2004)

• Sample period rather short: 10 years

(4) An illustration

• Descriptive approach

(4) An illustration

• Attractor approach

(4) An illustration

• Leading indicator approach

Almost impossible to beat an autoregressive process:• Poor forecasting performance of HICPX and trimmed mean

• A bit better for DFM

In the short-term: sectoral analysis

In the long-term: broad assessment

Conclusion

• Useful measures but mainly for descriptive purposes

• The one that has some information content on trend inflation is also very hard to communicate on

• Treatment of energy as pure noise is problematic (ECB, 2005)

Thank you